WO2020103570A1 - Image color correction method, device, storage medium and mobile terminal - Google Patents

Abstract

An image color correction method, device, storage medium and mobile terminal. The method comprises: acquiring an original image to be processed (101); inputting the original image into a pre-trained image color correction model (102); determining an output image of the image color correction model, and using the output image as a target image corresponding to the original image (103).

Description

Image color correction method, device, storage medium and mobile terminal

This application requires the priority of the Chinese patent application submitted to the China Patent Office on November 19, 2018, with the application number 201811377864.X and the invention titled "Image color correction method, device, storage medium and mobile terminal" Incorporated by reference in this application.

Technical field

Embodiments of the present application relate to the field of image processing technology, and in particular, to an image color correction method, device, storage medium, and mobile terminal.

Background technique

With the rapid development of mobile terminals, the requirements for the quality of images taken through the camera of the mobile terminal are becoming higher and higher. However, the color of the image collected by the camera is closely related to the collection environment. In different collection environments, the color of the image collected for the same collection target is different. The illumination of the collection environment and the RGB three components of the camera's image sensor will affect the final imaging color in response to objects of different colors. Therefore, in actual applications, the color of the image collected by the camera needs to be corrected to restore the collection target True colors. Therefore, an effective color correction method becomes critical to the quality of the image captured by the camera.

Summary of the invention

In a first aspect, an embodiment of the present application provides an image color correction method, including:

Get the original image to be processed;

Input the original image into a pre-trained image color correction model;

The output image of the image color correction model is determined, and the output image is used as the target image corresponding to the original image.

In a second aspect, an embodiment of the present application provides an image color correction device, including:

The original image acquisition module is used to acquire the original image to be processed;

A first original image input module, configured to input the original image into a pre-trained image color correction model;

The target image determination module is used to determine the output image of the image color correction model, and use the output image as the target image corresponding to the original image.

In a third aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored, which is implemented when executed by a processor:

Get the original image to be processed;

Input the original image into a pre-trained image color correction model;

The output image of the image color correction model is determined, and the output image is used as the target image corresponding to the original image.

According to a fourth aspect, an embodiment of the present application provides a terminal, including a memory, a processor, and a computer program stored on the memory and executable by the processor. The processor implements the computer program when it executes:

Get the original image to be processed;

Input the original image into a pre-trained image color correction model;

The output image of the image color correction model is determined, and the output image is used as the target image corresponding to the original image.

BRIEF DESCRIPTION

1 is a schematic flowchart of an image color correction method provided by an embodiment of the present application;

2 is a schematic flowchart of another image color correction method provided by an embodiment of the present application;

3 is a schematic flowchart of another image color correction method according to an embodiment of the present application;

4 is a schematic structural diagram of an image color correction device provided by an embodiment of the present application;

5 is a schematic structural diagram of a mobile terminal according to an embodiment of this application;

FIG. 6 is a schematic structural diagram of another mobile terminal according to an embodiment of the present application.

detailed description

The technical solution of the present application will be further described below with reference to the drawings and through specific implementations. It can be understood that the specific embodiments described herein are only used to explain the present application, rather than limit the present application. In addition, it should be noted that, for ease of description, the drawings only show parts, but not all structures related to the present application.

Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowchart describes the steps as sequential processing, many of the steps can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the steps can be rearranged. The processing may be terminated when its operation is completed, but may also have additional steps not included in the drawings. The processing may correspond to methods, functions, procedures, subroutines, subroutines, and so on.

CCM (ColorCorrectionMatrix, color correction matrix) is an important means to color correct the image, restore the image color, adjust the image style, and improve the image quality. In the traditional technology, different CCM matrices are mainly used for images in different scenes. Switching between different scenes of the image is easy to cause a sudden change in the image effect after color correction, so that the image adjustment effect cannot guarantee a consistent style, especially for the use of cameras to perform image The preview stage will greatly affect the user experience in practical applications. Based on the above considerations, the following image color correction solutions are now provided.

An embodiment of the present application provides an image color correction method, including:

Get the original image to be processed;

Input the original image into a pre-trained image color correction model;

The output image of the image color correction model is determined, and the output image is used as the target image corresponding to the original image.

In an embodiment, before acquiring the original image to be processed, the method further includes:

Acquiring the image color correction model;

Wherein, the image color correction model is obtained as follows:

Collect the first sample original image through the camera;

Performing color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image;

Using the first sample original image and the first sample target image as the first training sample set;

The first preset machine learning model is trained using the first training sample set to obtain an image color correction model.

In an embodiment, before performing color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image, the method further includes:

Acquiring a sample RGB image corresponding to the original image of the first sample through a camera;

Performing color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image includes:

Using the sample RGB image as a reference image, color correcting the first sample original image to obtain a first sample target image corresponding to the first sample original image.

In an embodiment, acquiring the original image of the first sample through the camera includes:

The first sample original image of the first standard color card under different illuminations is collected through a camera; wherein, the first standard color card is a color color card.

In an embodiment, acquiring the original image of the first sample through the camera further includes:

The first sample original image of at least two shooting scenes under different illuminations is collected by the camera.

In an embodiment, before inputting the original image into the pre-trained image color correction model, the method further includes:

Input the original image into a pre-trained white balance coefficient matrix determination model;

Determining the output result of the model according to the white balance coefficient matrix, and determining the white balance coefficient matrix corresponding to the original image;

Performing white balance processing on the original image according to the white balance coefficient matrix;

The inputting the original image into the pre-trained image color correction model includes:

The original image after the white balance processing is input into the pre-trained image color correction model.

In an embodiment, before inputting the original image into the pre-trained white balance coefficient matrix determination model, the method further includes:

Obtain the white balance coefficient matrix to determine the model;

The determination model of the white balance coefficient matrix is obtained as follows:

Acquiring a second sample original image of the second standard color card at different color temperatures through a camera; wherein, the second standard color card is a white color card;

Performing white balance processing on the second sample original image to obtain a second sample target image corresponding to the second sample original image;

Determine, according to the second sample original image and the second sample target image, change the second sample original image to a sample white balance coefficient matrix corresponding to the second sample target image;

Marking the original image of the second sample according to the sample white balance coefficient matrix to obtain a second training sample set;

Use the second training sample set to train a second preset machine learning model to obtain a white balance coefficient matrix determination model.

In an embodiment, performing white balance processing on the original image according to the white balance coefficient matrix includes:

Acquiring the first RGB component value of each pixel in the original image;

For all pixels in the original image, the product of the first RGB component value of each pixel and the white balance coefficient of the corresponding position in the white balance coefficient matrix is used as the target image corresponding to the pixel of the original image The second RGB component value of the pixel.

In an embodiment, after the output image is used as the target image corresponding to the original image, the method further includes:

Gamma correction is performed on the target image, and the target image after gamma correction is output.

FIG. 1 is a schematic flowchart of an image color correction method provided by an embodiment of the present application. The method may be executed by an image color correction device, where the device may be implemented by software and / or hardware, and may generally be integrated in a mobile terminal. As shown in Figure 1, the method includes:

Step 101: Acquire an original image to be processed.

Exemplarily, the mobile terminal in the embodiment of the present application may include a mobile device with a camera function such as a mobile phone, a tablet computer, and a video camera.

In the embodiment of the present application, when it is detected that the camera of the mobile terminal is in an open state, that is, when it is detected that the camera of the mobile terminal is in a preview state or an image is captured, the raw image collected by the camera is acquired. The raw image is used as the original image to be processed. Optionally, if the camera collects the raw image and performs white balance processing on the raw image based on a preset white balance processing algorithm, the raw image after the white balance processing may be used as the original image to be processed. Optionally, a raw image transmitted by another terminal device or an image to be color-corrected may also be obtained and used as the original image to be processed. Of course, the image to be color corrected can also be obtained directly from the image library stored in the mobile terminal as the original image to be processed. It should be noted that the source or acquisition method of the original image to be processed in the embodiment of the present application is not limited.

Optionally, when it is detected that the image color correction event is triggered, the original image to be processed is acquired. It can be understood that, in order to perform color correction on the image at an appropriate timing, the trigger condition of the image color correction event may be set in advance. Exemplarily, in order to meet the user's visual requirements for the collected image, an image color correction event may be triggered when it is detected that the camera is in the on state. Optionally, when the user is not satisfied with the contrast of an image in the mobile terminal, an image color correction event may be triggered when it is detected that the user actively opens the image color correction authority. Optionally, in order to apply the image color correction to more valuable application opportunities to save the additional power consumption caused by the image color correction, the application timing and application scenarios of the image color correction can be analyzed or investigated, etc., and the settings are reasonable , When detecting that the mobile terminal is in the preset scene, trigger an image color correction event. It should be noted that the embodiment of the present application does not limit the specific expression form in which the image color correction event is triggered.

Step 102: Input the original image into a pre-trained image color correction model.

In the embodiment of the present application, the image color correction model can be understood as the learning model of the target image corresponding to the original image to be processed quickly after inputting the original image to be processed, wherein the corresponding to the original image to be processed The target image is an image that has undergone image color correction on the original image. The image color correction model may be a learning model generated by training the collected sample original image and the image color correction image adjusted to the best effect. It is understandable that the image color correction model can be generated by learning the sample original image and the image color correction image that adjusts the sample original image to the best effect, and the correspondence between the two. The image color correction model is an end-to-end learning model, that is, a learning model where both input and output are images.

Step 103: Determine the output image of the image color correction model, and use the output image as a target image corresponding to the original image.

Exemplarily, after inputting the original image to be processed into the image color correction model, the image color correction model analyzes the original image to be processed, and performs color correction on the original image according to the analysis result to obtain the original image. The target image after color correction and output. It can be understood that, after the original image to be processed is input to the image color correction model, and the image color correction model is analyzed, and the image is directly output, the output image may be used as the target image corresponding to the original image. That is, the output image of the image color correction model is an image after the color correction model performs color correction on the original image to be processed, that is, a target image corresponding to the original image.

The image color correction method provided in the embodiment of the present application obtains an original image to be processed; input the original image into a pre-trained image color correction model; determine the output image of the image color correction model, and apply the The output image is used as the target image corresponding to the original image. By adopting the above technical solution, not only can the original image be color-corrected simply and quickly, but also the corresponding color correction of the different input original images can be targeted, which can effectively improve the quality of the image and further enhance the contrast of the image To make the image closer to the true color.

In some embodiments, after the output image is used as the target image corresponding to the original image, the method further includes: performing gamma correction on the target image, and outputting the target image after gamma correction. Exemplarily, the target image is obtained after color correction is performed on the original image. In order to further increase the contrast of the target image, the target image may be further gamma-corrected, and the gamma-corrected target image may be output. The advantage of this setting is that it can improve the color of the dark areas in the target image, which can further improve the contrast of the image and improve the quality of the image.

FIG. 2 is a schematic flowchart of an image color correction method provided by an embodiment of the present application. As shown in FIG. 2, the method includes:

Step 201: Collect the first sample original image through the camera.

Optionally, collecting the first sample original image through the camera includes: collecting the first sample original image of the first standard color card under different illumination through the camera; wherein, the first standard color card is a color color card; Alternatively, the first sample original image of at least two shooting scenes under different illuminations can be collected through a camera.

Exemplarily, the first standard color card is a color color card, for example, the first standard color card may be a standard color card with 24 pure color blocks of different colors, and images of the first standard color card under different illuminations are collected through a camera , As the first sample original image. Exemplarily, the raw images of the first standard color card at different color temperatures are collected through a camera, and the white balance processing is performed on the raw image based on a preset white balance processing algorithm, then the raw image after the white balance processing can be used as the first This original image.

For another example, at least two images of the shooting scene under different illuminations are collected through the camera, and the collected images are used as the first sample original images. Optionally, at least two shooting scenes preferably contain subjects of different colors to make the shooting scenes rich in color. It is understandable that different shooting scenes contain different colors of different shooting subjects, then the images of at least two shooting scenes under different illuminations are collected by the camera as the first sample original image, which can make the first sample taken original The image can not only simulate the image of the first standard color card collected by the camera under different illuminations, but also contain more scene information.

Step 202: Perform color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image.

In the embodiment of the present application, an existing image color correction method may be used to perform color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image. Optionally, input the original image of the first sample into the ISP (Image Signal Processor) tool, manually perform color correction adjustment on the original image of the first sample, and adjust to the image with the best color correction effect As the first sample target image corresponding to the first sample original image. Among them, when performing color correction adjustment on the original image of the first sample, whether the image adjusted to the best color correction effect can be confirmed by the first intuitive feeling of human eyes, and can also be evaluated by the image quality evaluation standard until the color is obtained Correct the best image.

Optionally, before performing color correction on the original image of the first sample to obtain a target image of the first sample corresponding to the original image of the first sample, the method further includes: collecting through the camera the same as the first A sample RGB image corresponding to the original image; performing color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image, including: taking the sample RGB image as With reference to the image, the first sample original image is color corrected to obtain a first sample target image corresponding to the first sample original image. The advantage of this setting is that it can effectively control the correction scale when performing color correction on the original image of the first sample, can easily and quickly complete the color correction on the original image of the first sample, and can achieve a better color correction effect .

Exemplarily, when the first sample original image (raw image or the image after the raw image is subjected to white balance processing) of the first standard color card under different illuminations is collected by the camera, the first standard color card can be collected by the camera Sample RGB images under different lighting, where the first sample original image corresponds to the sample RGB image one by one, that is, the first sample original image and the sample RGB image are both collected by the first standard color card under the same light image. Also for example, when the first sample original image (raw image or the image after the raw image is subjected to white balance processing) of at least two shooting scenes under different illuminations is collected by the camera, at least two shootings can be collected by the camera Sample RGB images of the scene under different lighting, where the first sample original image corresponds to the sample RGB image one by one, that is, the first sample original image and the sample RGB image are collected images of the same shooting scene under the same light .

When the first sample original image is color-corrected, the sample RGB image corresponding to the first sample original image is used as a reference image to achieve a better color correction effect on the first sample original image. Exemplarily, input the first sample original image into the ISP tool, use the sample RGB image as the reference image, and manually perform color correction on the first sample original image until the color corrected image is not very different from the sample RGB image When it is large, it can be considered that the original image of the first sample is adjusted to an image with better color correction effect.

Step 203: Use the first sample original image and the first sample target image as the first training sample set.

The first sample original image and the first sample target image corresponding to the first sample original image are used as the training sample set of the image color correction model, that is, the first training sample set.

Step 204: Use the first training sample set to train a first preset machine learning model to obtain an image color correction model.

Exemplarily, the first preset machine learning model is trained using the first training sample set to generate an image color correction model. The first preset machine learning model may include a machine learning model such as a convolutional neural network model or a long-term short-term memory network model, and may also include a naive Bayes model. It should be noted that the embodiment of the present application does not limit the first preset machine learning model.

Step 205: Acquire the original image to be processed.

Step 206: Input the original image into a pre-trained image color correction model.

Step 207: Determine the output image of the image color correction model, and use the output image as the target image corresponding to the original image.

Among them, before acquiring the original image to be processed, an image color correction model is acquired. It should be noted that the mobile terminal may obtain the first training sample set, use the first training sample set to train the first preset machine learning model, and directly generate an image color correction model. It may also be that the mobile terminal directly calls the image color correction model generated by the training of other mobile terminals. For example, before leaving the factory, a mobile terminal is used to obtain the first training sample set and generate an image color correction model, and then store the image color correction model to Among other mobile terminals, it is directly used by other mobile terminals. Or, the server obtains a large number of first sample original images and the first sample target images after color correction of the first sample original images to obtain a first training sample set. The server trains the first training sample set based on the first preset machine learning model to obtain an image color correction model. When the mobile terminal needs to perform image color correction, the trained image color correction model is called from the server.

The image color correction method provided in the embodiment of the present application obtains an original image to be processed, and inputs the original image into a pre-trained image color correction model, and then determines an output image of the image color correction model, and The output image is used as the target image corresponding to the original image, wherein the image color correction model is generated based on the first sample original image and the first sample target image after color correction of the first sample original image is generated by training of. By adopting the above technical solution, the first sample original image of the first standard color card collected under different lighting, or the first sample original image of at least two shooting scenes collected under different lighting, and the first The first sample target image after color correction of the original sample image, training and learning of the image color correction model can effectively improve the accuracy of the image color correction model, and the image color correction model can be accurately and quickly processed Color correction of the original image can effectively improve the image quality.

FIG. 3 is a schematic flowchart of an image color correction method provided by an embodiment of the present application. As shown in FIG. 3, the method includes:

Step 301: Collect the first sample original image of the first standard color card under different illumination through the camera; wherein the first standard color card is a color color card; or collect at least two shooting scenes under different illumination through the camera The first sample of the original image.

Step 302: Perform color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image.

Step 303: Use the first sample original image and the first sample target image as the first training sample set.

Step 304: Use the first training sample set to train the first preset machine learning model to obtain an image color correction model.

Step 305: Acquire the original image to be processed.

Exemplarily, the original image to be processed includes the raw image collected by the camera. Or, the original image to be processed includes an image that needs to be color corrected, but in order to achieve a better color correction effect, the original image needs to be subjected to white balance processing in advance.

Step 306: Input the original image into a pre-trained white balance coefficient matrix determination model.

In the embodiment of the present application, the white balance coefficient matrix determination model can be understood as a learning model for quickly determining the white balance coefficient matrix corresponding to the original image to be processed after inputting the original image to be processed. The white balance coefficient matrix determination model may be a learning model generated by training the collected second sample original image and the corresponding sample white balance coefficient matrix, where the sample white balance coefficient matrix includes the white Balance the matrix when processing images. It can be understood that the white balance coefficient matrix determination model can be generated by learning the second sample original image and the corresponding sample white balance coefficient matrix, and the correspondence between the two.

Step 307: Determine the output result of the model according to the white balance coefficient matrix, and determine the white balance coefficient matrix corresponding to the original image.

Exemplarily, after inputting the original image to be processed into the white balance coefficient matrix determination model, the white balance coefficient matrix determination model analyzes the original image, and determines the white balance coefficient matrix corresponding to the original image to be processed according to the analysis result .

Step 308: Perform white balance processing on the original image according to the white balance coefficient matrix.

Exemplarily, the original image to be processed is subjected to white balance processing based on the white balance coefficient matrix. For example, the product of the original image and the white balance coefficient matrix may be used as the image after the white balance processing is performed on the original image.

Optionally, performing white balance processing on the original image according to the white balance coefficient matrix includes: acquiring the first RGB component value of each pixel in the original image; for all pixels in the original image, The product of the first RGB component value of each pixel and the white balance coefficient of the corresponding position in the white balance coefficient matrix is used as the second RGB component value of the pixel of the target image corresponding to the pixel of the original image. The advantage of this setting is that it can determine an independent white balance coefficient for each pixel in the original image to be processed, and perform white balance processing on each pixel in the original image based on the white balance coefficient matrix, which can solve the problem based on the global white When the balance algorithm performs white balance processing, it is easy to cause the color deviation of the solid color object to be large, and the technical problem that the white block cannot be accurately detected at the mixed color temperature can effectively improve the image quality and increase the saturation of the image.

Exemplarily, the first RGB component value of each pixel in the original image is obtained, and for all pixels in the original image, the first RGB component value of each pixel is multiplied by the corresponding position in the white balance coefficient matrix White balance coefficient, and the result of the product as the second RGB component value of the target image pixel corresponding to the pixel of the original image, that is, the result of the product as the pixel of the original image after white balance processing The second RGB component value. Exemplarily, the first RGB component value of the first pixel in the original image (the pixel in the first row and first column of the original image) is obtained, and then the white balance of the first row and first column in the white balance coefficient matrix The product of the coefficient and the first RGB component value of the first pixel is used as the first pixel in the image after the white balance processing of the original image (the first row and first column of the white balance processed image) Pixels) the second RGB component value. By analogy, based on the white balance coefficient matrix, a similar processing operation is performed on each pixel in the original image, thereby obtaining an image after the original image is subjected to white balance processing.

Step 309: Input the original image after the white balance processing into the pre-trained image color correction model.

In the embodiment of the present application, the original image after the white balance process is input into the image color correction model, and the image color correction model analyzes the image to perform color correction.

Step 310: Determine the output image of the image color correction model, and use the output image as the target image corresponding to the original image.

Step 311: Perform gamma correction on the target image, and output the target image after gamma correction.

The image color correction method provided in the embodiment of the present application obtains an original image to be processed, inputs the original image into a pre-trained white balance coefficient matrix determination model, and determines the output result of the model according to the white balance coefficient matrix to determine the original image Corresponding white balance coefficient matrix, then perform white balance processing on the original image according to the white balance coefficient matrix, input the original image after the white balance processing into the pre-trained image color correction model, and determine the output image of the image color correction model , Use the output image as the target image corresponding to the original image. By adopting the above technical solution, the white balance coefficient matrix determination model can be used to perform white balance processing on the original image, and the image color correction model can be used to color correct the image after the white balance processing, which can not only improve the original image contrast, but Increasing the saturation of the image can effectively improve the image quality.

In some embodiments, before inputting the original image into the pre-trained white balance coefficient matrix determination model, the method further includes: acquiring a white balance coefficient matrix determination model; wherein, the white balance coefficient matrix determination model is as follows Obtained: the second sample original image of the second standard color card at different color temperatures is collected through the camera; wherein, the second standard color card is a white color card; the white balance processing is performed on the second sample original image to obtain A second sample target image corresponding to the second sample original image; based on the second sample original image and the second sample target image, determining to change the second sample original image to the second sample target image Corresponding sample white balance coefficient matrix; marking the original image of the second sample according to the sample white balance coefficient matrix to obtain a second training sample set; using the second training sample set to the second preset machine learning model After training, a model for determining the white balance coefficient matrix is obtained.

In the embodiment of the present application, the second standard color card is a white color card, and images of the second standard color card at different color temperatures are collected by a camera as the second sample original image. Exemplarily, a raw image of a standard color card at different color temperatures is collected by a camera as a second sample original image. Different color temperatures can be achieved by artificial light sources. Exemplarily, in a laboratory environment, different types of light sources are used to create different color temperature environments. For example, using candles as a light source can create a color temperature environment of 2000k, using high-pressure sodium lamps as a light source can create a color temperature environment of 1950-2250k, using a tungsten filament lamp as a light source can create a color temperature environment of 2700k, and using a halogen lamp as a light source Create a color temperature environment of 3000k, and use a warm fluorescent lamp as a light source to create a color temperature environment of 4000k-4600k. A series of shooting environments with continuous color temperature values can be provided by different types of light sources. The second standard color card is photographed at different color temperatures using a camera to obtain color card images at each color temperature, thereby obtaining a second sample original image of the second standard color card at different color temperatures.

Exemplarily, the existing white balance processing method may be used to perform white balance processing on the second sample original image to obtain a second sample target image corresponding to the second sample original image. Optionally, input the second sample original image into the ISP tool, manually perform white balance adjustment on the second sample original image, and use the image adjusted to the best white balance effect as the second sample corresponding to the second sample original image Target image. Among them, when the white balance adjustment is performed on the original image of the second sample, whether the image adjusted to the best white balance effect can be confirmed by the second intuitive feeling of the human eye, and can also be evaluated by the image quality evaluation standard until the white balance is obtained The best image.

In the embodiment of the present application, according to the second sample original image and the second sample target image corresponding to the second sample original image, the corresponding sample white balance coefficient is determined when the second sample original image is changed to the second sample target image Matrix, that is, the matrix of white balance coefficients used in the process of white balance when the white balance processing is performed on the original image of the second sample to obtain the target image of the second sample.

Optionally, determining, according to the second sample original image and the second sample target image, to change the second sample original image to a sample white balance coefficient matrix corresponding to the second sample target image, includes: obtaining The third RGB component value of each pixel in the second sample original image and the fourth RGB component value of each pixel in the second sample target image; for all pixels, the corresponding The ratio of the fourth RGB component value to the third RGB component value is used as the white balance coefficient corresponding to the pixel point in the sample white balance coefficient matrix. The advantage of this setting is that it can accurately determine the corresponding white balance coefficient matrix when performing white balance processing on the original image of the second standard color card under different color temperature environments.

Exemplarily, the third RGB component value of each pixel in the second sample original image and the fourth RGB component value of each pixel in the second sample target image are obtained respectively, and for each pixel, the corresponding pixel is calculated separately The ratio of the fourth RGB component value of the point to the third RGB component value, and using this ratio as the white balance coefficient matrix of the pixel point. Exemplarily, obtain the third RGB component value of the first pixel in the second sample original image (the pixel in the first row and first column in the second sample original image), and the first in the second sample target image The fourth RGB component value of the pixel point (the pixel point in the first row and first column in the first sample target image), and the ratio of the fourth RGB component value to the third RGB component value is used as the white balance coefficient matrix The white balance coefficient in the first row and first column of. In the above manner, by analogy, the white balance coefficients of each element in the white balance coefficient matrix are determined respectively.

Exemplarily, according to each sample white balance coefficient matrix obtained, the corresponding second sample original image is marked respectively, and the second sample original image corresponding to the sample white balance coefficient matrix is marked as the white balance coefficient matrix determination model Training sample set, which is the second training sample set. Exemplarily, the second preset machine learning model is trained using the second training sample set to generate a white balance coefficient matrix determination model. The second preset machine learning model may include a machine learning model such as a convolutional neural network model or a long-term and short-term memory network model. The embodiment of the present application does not limit the second preset machine learning model, where the second preset machine learning model and the first preset machine learning model may be the same or different.

Before acquiring the original image to be processed, a white balance coefficient matrix determination model is acquired. It should be noted that the mobile terminal may obtain the second training sample set, use the second training sample set to train the second preset machine learning model, and directly generate a white balance coefficient matrix determination model. It may also be that the mobile terminal directly calls the white balance coefficient matrix determination model generated by other mobile terminal training, for example, before leaving the factory, a mobile terminal is used to obtain a second training sample set and generate a white balance coefficient matrix determination model, and then the white balance coefficient The matrix determination model is stored in other mobile terminals for direct use by other mobile terminals. Alternatively, the server obtains a large number of second sample original images and a white balance coefficient matrix corresponding to the second sample original image, and marks the second sample original images according to the corresponding white balance coefficient matrix to obtain a second training sample set. The server trains the second training sample set based on the second preset machine learning model to obtain a white balance coefficient matrix determination model. When the mobile terminal needs to perform image white balance processing, the trained white balance coefficient matrix is called from the server to determine the model.

FIG. 4 is a schematic structural diagram of an image color correction device provided by an embodiment of the present application. The device may be implemented by software and / or hardware, and is generally integrated in a mobile terminal. The original image to be processed may be processed by performing an image color correction method. Color correction. As shown in Figure 4, the device includes:

The original image acquisition module 401 is used to acquire an original image to be processed;

The first original image input module 402 is used to input the original image into a pre-trained image color correction model;

The target image determination module 403 is configured to determine an output image of the image color correction model, and use the output image as a target image corresponding to the original image.

The image color correction device provided in the embodiment of the present application acquires an original image to be processed; input the original image into a pre-trained image color correction model; determine the output image of the image color correction model, and apply the The output image is used as the target image corresponding to the original image. By adopting the above technical solutions, not only can the original image be color-corrected simply and quickly, but also the corresponding color correction can be applied to different input original images in a targeted manner, which can effectively improve the quality of the image and make the image closer to the real color.

Optionally, the device further includes:

A color correction model obtaining module, used to obtain the image color correction model before obtaining the original image to be processed;

Wherein, the image color correction model is obtained as follows:

Collect the first sample original image through the camera;

Performing color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image;

Using the first sample original image and the first sample target image as the first training sample set;

The first preset machine learning model is trained using the first training sample set to obtain an image color correction model.

Optionally, before performing color correction on the original image of the first sample to obtain a target image of the first sample corresponding to the original image of the first sample, the method further includes:

Acquiring a sample RGB image corresponding to the original image of the first sample through a camera;

Performing color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image includes:

Using the sample RGB image as a reference image, color correcting the first sample original image to obtain a first sample target image corresponding to the first sample original image.

Optionally, collecting the first sample original image through the camera, including:

Collect the first sample original image of the first standard color card under different illumination through the camera; wherein, the first standard color card is a color color card; or

The first sample original image of at least two shooting scenes under different illuminations is collected by the camera.

Optionally, the device further includes:

A second original image input module, configured to input the original image into the pre-trained white balance coefficient matrix determination model before inputting the original image into the pre-trained image color correction model;

A white balance coefficient matrix determination module, configured to determine an output result of the model according to the white balance coefficient matrix, and determine a white balance coefficient matrix corresponding to the original image;

A white balance processing module, configured to perform white balance processing on the original image according to the white balance coefficient matrix;

The first original image input module is used to:

The original image after the white balance processing is input into the pre-trained image color correction model.

Optionally, the device further includes:

The coefficient matrix determination model acquisition module is used to acquire the white balance coefficient matrix determination model before inputting the original image into the pre-trained white balance coefficient matrix determination model;

The determination model of the white balance coefficient matrix is obtained as follows:

Acquiring a second sample original image of the second standard color card at different color temperatures through a camera; wherein, the second standard color card is a white color card;

Performing white balance processing on the second sample original image to obtain a second sample target image corresponding to the second sample original image;

Determine, according to the second sample original image and the second sample target image, change the second sample original image to a sample white balance coefficient matrix corresponding to the second sample target image;

Marking the original image of the second sample according to the sample white balance coefficient matrix to obtain a second training sample set;

Use the second training sample set to train a second preset machine learning model to obtain a white balance coefficient matrix determination model.

Optionally, the device further includes:

The gamma correction module is used to perform gamma correction on the target image after the output image is used as the target image corresponding to the original image, and output the target image after gamma correction.

Embodiments of the present application also provide a storage medium containing computer-executable instructions, which when executed by a computer processor are used to perform an image color correction method, the method includes:

Get the original image to be processed;

Input the original image into a pre-trained image color correction model;

The output image of the image color correction model is determined, and the output image is used as the target image corresponding to the original image.

Storage medium-any kind of memory device or storage device. The term "storage medium" is intended to include: installation media such as CD-ROM, floppy disk or tape devices; computer system memory or random access memory such as DRAM, DDRRAM, SRAM, EDORAM, Rambus RAM, etc .; Volatile memory, such as flash memory, magnetic media (such as hard disks or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or a combination thereof. In addition, the storage medium may be located in the first computer system in which the program is executed, or may be located in a different second computer system that is connected to the first computer system through a network such as the Internet. The second computer system may provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations (eg, in different computer systems connected through a network). The storage medium may store program instructions executable by one or more processors (eg, embodied as a computer program).

Of course, a storage medium containing computer-executable instructions provided by the embodiments of the present application is not limited to the image color correction operation described above, and can also perform the image color correction provided by any embodiment of the present application Related operations in the method.

An embodiment of the present application provides a mobile terminal, and the image color correction device provided by the embodiment of the present application can be integrated into the mobile terminal. FIG. 5 is a schematic structural diagram of a mobile terminal according to an embodiment of the present application. The mobile terminal 500 may include: a memory 501, a processor 502, and a computer program stored on the memory and executable by the processor. When the processor 502 executes the computer program, image color correction as described in the embodiments of the present application is implemented method.

The mobile terminal provided by the embodiment of the present application can not only perform color correction on the original image simply and quickly, but also can perform corresponding color correction on different input original images in a targeted manner, which can effectively improve the quality of the image and make the image Closer to true colors.

FIG. 6 is a schematic structural diagram of another mobile terminal provided by an embodiment of the present application. The mobile terminal may include: a casing (not shown in the figure), a memory 601, and a central processing unit (CPU) 602 (also Called processor, hereinafter referred to as CPU), circuit board (not shown in the figure) and power supply circuit (not shown in the figure). The circuit board is disposed inside the space enclosed by the housing; the CPU 602 and the memory 601 are provided on the circuit board; and the power circuit is used to supply power to various circuits or devices of the mobile terminal The memory 601 is used to store executable program code; the CPU 602 runs the computer program corresponding to the executable program code by reading the executable program code stored in the memory 601 to achieve the following steps:

Get the original image to be processed;

Input the original image into a pre-trained image color correction model;

The output image of the image color correction model is determined, and the output image is used as the target image corresponding to the original image.

The mobile terminal also includes: peripheral interface 603, RF (Radio Frequency) circuit 605, audio circuit 606, speaker 611, power management chip 608, input / output (I / O) subsystem 609, other input / control The device 610, the touch screen 612, other input / control devices 610, and the external port 604 communicate through one or more communication buses or signal lines 607.

It should be understood that the illustrated mobile terminal 600 is only an example of the mobile terminal, and the mobile terminal 600 may have more or fewer components than shown in the figure, and two or more components may be combined, Or it can have different component configurations. The various components shown in the figures may be implemented in hardware, software, or a combination of hardware and software, including one or more signal processing and / or application specific integrated circuits.

The mobile terminal for image color correction provided in this embodiment will be described in detail below. The mobile terminal uses a mobile phone as an example.

Memory 601, which can be accessed by CPU 602, peripheral interface 603, etc. The memory 601 can include high-speed random access memory, and can also include non-volatile memory, such as one or more disk storage devices, flash memory devices , Or other volatile solid-state storage devices.

Peripheral interface 603, which can connect input and output peripherals of the device to CPU 602 and memory 601.

I / O subsystem 609, which can connect input and output peripherals on the device, such as touch screen 612 and other input / control devices 610, to peripheral interface 603. The I / O subsystem 609 may include a display controller 6091 and one or more input controllers 6092 for controlling other input / control devices 610. Among them, one or more input controllers 6092 receive electrical signals from other input / control devices 610 or send electrical signals to other input / control devices 610, and other input / control devices 610 may include physical buttons (press buttons, rocker buttons, etc.) ), Dial pad, slide switch, joystick, click wheel. It is worth noting that the input controller 6092 can be connected to any of the following: a keyboard, an infrared port, a USB interface, and a pointing device such as a mouse.

A touch screen 612, which is an input interface and an output interface between the user's mobile terminal and the user, and displays visual output to the user, and the visual output may include graphics, text, icons, video, and the like.

The display controller 6091 in the I / O subsystem 609 receives electrical signals from the touch screen 612 or sends electrical signals to the touch screen 612. The touch screen 612 detects the contact on the touch screen, and the display controller 6091 converts the detected contact into interaction with the user interface object displayed on the touch screen 612, that is, realizes human-computer interaction, and the user interface object displayed on the touch screen 612 may be running Icons for games, icons connected to the corresponding network, etc. It is worth noting that the device may also include a light mouse, which is a touch-sensitive surface that does not display visual output or an extension of the touch-sensitive surface formed by a touch screen.

The RF circuit 605 is mainly used to establish communication between the mobile phone and the wireless network (that is, the network side), and to realize data reception and transmission between the mobile phone and the wireless network. For example, sending and receiving short messages, e-mail, etc. Specifically, the RF circuit 605 receives and transmits RF signals, which are also called electromagnetic signals. The RF circuit 605 converts electrical signals into electromagnetic signals or converts electromagnetic signals into electrical signals, and communicates with the communication network and other devices through the electromagnetic signals Communicate. The RF circuit 605 may include known circuits for performing these functions, including but not limited to antenna systems, RF transceivers, one or more amplifiers, tuners, one or more oscillators, digital signal processors, CODEC ( COder-DECoder (codec) chipset, subscriber identity module (Subscriber Identity Module, SIM), etc.

The audio circuit 606 is mainly used to receive audio data from the peripheral interface 603, convert the audio data into electrical signals, and send the electrical signals to the speaker 611.

The speaker 611 is used to restore the voice signal received by the mobile phone from the wireless network through the RF circuit 605 to a sound and play the sound to the user.

The power management chip 608 is used for power supply and power management for the hardware connected to the CPU 602, the I / O subsystem, and the peripheral interface.

The image color correction device, storage medium, and mobile terminal provided in the above embodiments can execute the image color correction method provided in any embodiment of the present application, and have corresponding function modules and beneficial effects for performing the method. For technical details that are not described in detail in the above embodiments, please refer to the image color correction method provided in any embodiment of the present application.

Note that the above are only the preferred embodiments of the present application and the technical principles applied. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that those skilled in the art can make various obvious changes, readjustments and substitutions without departing from the scope of protection of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application. The scope is determined by the scope of the appended claims.

Claims (20)

1. An image color correction method, characterized in that it includes:

   Get the original image to be processed;

   Input the original image into a pre-trained image color correction model;

   The output image of the image color correction model is determined, and the output image is used as the target image corresponding to the original image.
2. The method according to claim 1, wherein before acquiring the original image to be processed, further comprising:

   Acquiring the image color correction model;

   Wherein, the image color correction model is obtained as follows:

   Collect the first sample original image through the camera;

   Performing color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image;

   Using the first sample original image and the first sample target image as the first training sample set;

   The first preset machine learning model is trained using the first training sample set to obtain an image color correction model.
3. The method according to claim 2, wherein before performing color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image, further comprising:

   Acquiring a sample RGB image corresponding to the original image of the first sample through a camera;

   Performing color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image includes:

   Using the sample RGB image as a reference image, color correcting the first sample original image to obtain a first sample target image corresponding to the first sample original image.
4. The method according to claim 2, wherein acquiring the original image of the first sample through the camera includes:

   The first sample original image of the first standard color card under different illuminations is collected through a camera; wherein, the first standard color card is a color color card.
5. The method according to claim 2, wherein collecting the original image of the first sample through the camera further comprises:

   The first sample original image of at least two shooting scenes under different illuminations is collected by the camera.
6. The method according to claim 1, wherein before inputting the original image into the pre-trained image color correction model, further comprising:

   Input the original image into a pre-trained white balance coefficient matrix determination model;

   Determining the output result of the model according to the white balance coefficient matrix, and determining the white balance coefficient matrix corresponding to the original image;

   Performing white balance processing on the original image according to the white balance coefficient matrix;

   The inputting the original image into the pre-trained image color correction model includes:

   The original image after the white balance processing is input into the pre-trained image color correction model.
7. The method according to claim 6, wherein before inputting the original image into a pre-trained white balance coefficient matrix determination model, the method further comprises:

   Obtain the white balance coefficient matrix to determine the model;

   The determination model of the white balance coefficient matrix is obtained as follows:

   Acquiring a second sample original image of the second standard color card at different color temperatures through a camera; wherein, the second standard color card is a white color card;

   Performing white balance processing on the second sample original image to obtain a second sample target image corresponding to the second sample original image;

   Determine, according to the second sample original image and the second sample target image, change the second sample original image to a sample white balance coefficient matrix corresponding to the second sample target image;

   Marking the original image of the second sample according to the sample white balance coefficient matrix to obtain a second training sample set;

   Use the second training sample set to train a second preset machine learning model to obtain a white balance coefficient matrix determination model.
8. The method according to claim 6, wherein performing white balance processing on the original image according to the white balance coefficient matrix includes:

   Acquiring the first RGB component value of each pixel in the original image;

   For all pixels in the original image, the product of the first RGB component value of each pixel and the white balance coefficient of the corresponding position in the white balance coefficient matrix is used as the target image corresponding to the pixel of the original image The second RGB component value of the pixel.
9. The method according to claim 1, wherein after the output image is used as the target image corresponding to the original image, further comprising:

   Gamma correction is performed on the target image, and the target image after gamma correction is output.
10. An image color correction device, characterized in that it includes:

    The original image acquisition module is used to acquire the original image to be processed;

    A first original image input module, configured to input the original image into a pre-trained image color correction model;

    The target image determination module is used to determine the output image of the image color correction model, and use the output image as the target image corresponding to the original image.
11. A computer-readable storage medium on which a computer program is stored, characterized in that the program is implemented when executed by a processor:

    Get the original image to be processed;

    Input the original image into a pre-trained image color correction model;

    The output image of the image color correction model is determined, and the output image is used as the target image corresponding to the original image.
12. A mobile terminal is characterized by including a memory, a processor, and a computer program stored on the memory and executable by the processor. The processor implements the computer program when it executes:

    Get the original image to be processed;

    Input the original image into a pre-trained image color correction model;

    The output image of the image color correction model is determined, and the output image is used as the target image corresponding to the original image.
13. The mobile terminal according to claim 12, wherein before acquiring the original image to be processed, the processor further executes:

    Collect the first sample original image through the camera;

    Performing color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image;

    Using the first sample original image and the first sample target image as the first training sample set;

    The first preset machine learning model is trained using the first training sample set to obtain an image color correction model.
14. The mobile terminal according to claim 13, wherein before performing color correction on the first sample original image to obtain a first sample target image corresponding to the first sample original image, the The processor also executes:

    Acquiring a sample RGB image corresponding to the original image of the first sample through a camera;

    When performing color correction on the original image of the first sample to obtain a target image of the first sample corresponding to the original image of the first sample, the processor is configured to execute:

    Using the sample RGB image as a reference image, color correcting the first sample original image to obtain a first sample target image corresponding to the first sample original image.
15. The mobile terminal according to claim 13, characterized in that, when acquiring the original image of the first sample through the camera, the processor is configured to execute:

    The first sample original image of the first standard color card under different illuminations is collected through a camera; wherein, the first standard color card is a color color card.
16. The mobile terminal according to claim 13, wherein when the first sample original image is collected by the camera, the processor is further configured to execute:

    The first sample original image of at least two shooting scenes under different illuminations is collected by the camera.
17. The mobile terminal according to claim 12, wherein before the original image is input into the pre-trained image color correction model, the processor is further configured to execute:

    Input the original image into a pre-trained white balance coefficient matrix determination model;

    Determining the output result of the model according to the white balance coefficient matrix, and determining the white balance coefficient matrix corresponding to the original image;

    Performing white balance processing on the original image according to the white balance coefficient matrix;

    When inputting the original image into the pre-trained image color correction model, the processor is used to execute:

    The original image after the white balance processing is input into the pre-trained image color correction model.
18. The mobile terminal according to claim 17, wherein before inputting the original image into a pre-trained white balance coefficient matrix determination model, the processor is further configured to execute:

    Acquiring a second sample original image of the second standard color card at different color temperatures through a camera; wherein, the second standard color card is a white color card;

    Performing white balance processing on the second sample original image to obtain a second sample target image corresponding to the second sample original image;

    Determine, according to the second sample original image and the second sample target image, change the second sample original image to a sample white balance coefficient matrix corresponding to the second sample target image;

    Marking the original image of the second sample according to the sample white balance coefficient matrix to obtain a second training sample set;

    Use the second training sample set to train a second preset machine learning model to obtain a white balance coefficient matrix determination model.
19. The mobile terminal according to claim 17, wherein when performing white balance processing on the original image according to the white balance coefficient matrix, the processor is configured to execute:

    Acquiring the first RGB component value of each pixel in the original image;

    For all pixels in the original image, the product of the first RGB component value of each pixel and the white balance coefficient of the corresponding position in the white balance coefficient matrix is used as the target image corresponding to the pixel of the original image The second RGB component value of the pixel.
20. The mobile terminal according to claim 12, wherein after the output image is used as the target image corresponding to the original image, the processor is further configured to execute:

    Gamma correction is performed on the target image, and the target image after gamma correction is output.

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